Even though declines in shark populations jeopardise their ecological and economic services, there has been a significant paucity of data on shark demographics and behavioural ecology to reliably inform management decisions on sustainable use. This is primarily due to the concealing nature of the marine environment and logistical difficulties of systematic study, combined with their lack of historical commercial importance (Gruber & Myrberg 1977). In particular simple knowledge on shark spatial dynamics - where they are, when and importantly why - is lacking for many species. Emphasising the value of such information, previous research has shown that management interventions have been less effective when the spatial, or temporal, scales of species movements were not accounted for (Thirgood et al. 2004; Moffitt et al. 2009). In recent years, however, the application of remote telemetry, using both acoustic and satellite-linked transmitters, has started to provide insights on shark behaviour and habitat use that are of significant management value (Sims, 2010).
Remote telemetry has revealed shark behaviour to be much more varied and complicated than previously thought, including the capacity of several shark species to undertake large scale migrations that span ocean basins, traversing political boundaries and the high seas (Chapman et al. 2015). For instance, a white shark was recorded to travel between South Africa and Australia, covering over 10,000 km in 99 days (Bonfil et al. 2005). An individual basking shark Cetorhinus maximus was recorded moving across a similar distance between the UK and Canada (Gore et al. 2008), while basking sharks in the western Atlantic undertake trans-equatorial migrations that extend their known range into tropical waters (Skomal et al. 2009).
Likewise whale sharks Rhincodon typus travel widely throughout the Indian and Pacific Oceans (Eckert & Stewart 2001; Rowat & Gore 2007). In the Pacific a comprehensive, multispecies tracking programme has revealed comparative large scale movements, such as seasonal north/south migrations by the salmon shark Lamna ditropis (Block et al. 2011). Together these
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studies exemplify the need for the management of some species to be framed at an international scale as isolated local efforts may prove ineffective, emphasising the importance of initiatives such as the Convention on Migratory Species.
Tracking shark movements can also help identify areas of temporal significance for reproduction and foraging in wide ranging species (Sims 2010; Block et al. 2011). This can then be used to evaluate the effectiveness of management efforts such as time-area closures and gear mitigation to reduce bycatch (Block et al. 2011). For instance, discovery of previously unknown seasonal pupping by porbeagle shark Lamna nasus in the Sargasso Sea revealed a candidate for time-area closure (Campana, Joyce & Fowler 2010), whilst learning that common thresher sharks Alopias vulpinus off California mainly swim near the surface at night suggested that setting nocturnal drift-gillnets for broadbill swordfish Xiphias gladius marginally deeper could reduce bycatch (Cartamil et al. 2010). In the northeast Atlantic, an overlap of 76–100%
was reported between blue shark Prionace glauca nocturnal diving depths and the hook depths of vessels longlining for tuna and swordfish species (Queiroz et al. 2012). Such areas of high space-use overlap may also be targets for management efforts such as MPAs or changes in fishing practice to reduce blue shark bycatch (Queiroz et al. 2012). More recently, space-use of long-line vessels across the north Atlantic Ocean has been shown to overlap with hotspots of shark movements by 80%, with both associating with steep environmental gradients, such as thermal fronts (Queiroz et al. 2016). Such high overlap over an entire ocean basin scale may prohibit spatial management options, with alterations to fishing gear (e.g. monofilament leaders that sharks can bite through) or catch quotas/size limits potentially proving more effective (Queiroz et al. 2016).
Conversely some species display highly restricted movements that could exacerbate declines through lack of recruitment from adjacent populations (Robbins et al. 2006), especially in
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remote locations (Graham et al. 2010), but at the same time help target management efforts, such as MPAs, on areas of predictable use. For example, blacktip reef sharks Carcharhinus melanopterus tracked at Palmyra Atoll in the Pacific had a mean home range size of only 0.55 km2 (Papastamatiou et al. 2009). In similarly remote locations grey reef sharks Carcharhinus amblyrhynchos also displayed very confined movements (Field et al. 2011; Barnett et al. 2012), although on less isolated reefs they may range more widely (Heupel, Simpfendorfer &
Fitzpatrick 2010; Barnett et al. 2012). Other reef sharks, such as the whitetip reef and silvertip Carcharhinus albimarginatus, have also been shown to display high fidelity to particular reefs (Barnett et al. 2012).
Detailed knowledge of movements and habitat use can inform the efficacy of existing and planned MPAs (Edgar et al. 2014; Allen & Singh 2016). Even prior to the aforementioned discovery of basking shark migrations, it had been estimated that basking sharks spent on average only 22% of their time within protected British (territorial) waters, accentuating the need for international collaboration (Southall et al. 2006). Tracking of Caribbean reef Carcharhinus perezei and nurse sharks Ginglymostoma cirratum in the already established MPA of Glover’s Atoll, Belize, found that tagged sharks on average spent at least 32% of their time outside of the no-take zone, leaving them vulnerable to fishing and suggesting that the reserve design should be reconsidered (Chapman et al. 2005). However, the value of even this partial protection has since been demonstrated using baited camera traps, where Caribbean reef sharks were encountered 3–10 times more frequently in the Glover’s Atoll MPA than fished areas of the Mesoamerican Barrier Reef, although it is uncertain whether this is a function of decreased mortality or increased prey availability, or both (Bond et al. 2012). The need for informed reserve design is highlighted by the continued decline of reef shark populations on the Great Barrier Reef despite the use of MPAs (Robbins et al. 2006). Here, more easily policed no-entry zones contained more sharks than no-take zones, suggesting
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continued poaching and emphasising that legislation requires enforcement to be effective, which is often lacking due to limited resources (Robbins et al. 2006; Edgar et al. 2014).
The efficacy of MPAs also depends on temporal variation in MPA use by the target species and the surrounding fisheries (Edgar et al. 2014), making it necessary to obtain long-term, multi-year tracks to detect changes in movement behaviour over time (Allen & Singh 2016). For instance, in coastal east Australia, spottail Carcharhinus sorrah and juvenile pigeye sharks Carcharhinus amboinensis were found to spend on average 32% and 22% of their time, respectively, within two MPAs in Cleveland Bay, but this use varied seasonally, with spottail shark use peaking during winter and pigeye shark use during summer, potentially changing interactions with adjacent fisheries (Knip, Heupel & Simpfendorfer 2012). Sexual disparities in MPA use were also recorded for spottail sharks, highlighting the need for an appreciation of differing space use between sexes when considering management (Knip et al. 2012), particularly as spatio-temporal sexual segregation is common in many shark populations (Mucientes et al. 2009; Wearmouth & Sims 2010). Similar to Glover’s Atoll, despite only partly encompassing shark movements, the Cleveland Bay MPAs may afford the sharks some level of protection, albeit for pigeye sharks this is only for early life stages (Knip et al. 2012).
Modelling can be used to clarify what factors might drive shark movements, working towards a framework for better predicting movements in space and time. For example, a variety of shark species have been demonstrated to switch between differing optimal foraging strategies according to the distribution of resources available (Humphries et al. 2010; Sims et al. 2012).
However, recent reviews on tracking studies reveal how remarkably few attempt to relate observed patterns in shark movement to driving factors in this manner (the ‘why’), with many simply reporting the ‘where’ and ‘when’ (Sims 2010; Hammerschlag, Gallagher & Lazarre 2011). Whilst the latter are most certainly of management use, their power to predict shark
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movements is greatly increased if understood in the context of driving environmental factors, and subsequently how shark space-use may change over time (Humphries et al. 2010; Sims 2010).
Numerous factors have been proposed to influence shark movement patterns, including their physical condition (Gurshin & Szedlmayer 2004), water temperature (Sims et al. 2006), time of day (Shepard et al. 2006), currents (Rowat & Gore 2007), light levels (Nelson et al. 1997), time of year (Weng et al. 2008), geographic location (Stokesbury et al. 2005), topographical features (Holland et al. 1999), geomagnetic gradients (Klimley 1993), prey availability (Goldman &
Anderson 1999) and oxygen levels (Graham, Roberts & Smart 2006). But overall, from studies that have attempted to address the ‘why’ behind the dynamic nature of observed movements, water temperature has been revealed as a particularly important driver of shark space-use (Weng et al. 2008; Abascal et al. 2011; Block et al. 2011; Queiroz et al. 2016), while areas with steep thermal gradients and high primary productivity have been demonstrated to support high shark abundance and diversity (Worm, Lotze & Myers 2003; Sims 2010; Block et al. 2011;
Queiroz et al. 2012). Such an appreciation of shark environmental preferences can then be used to predict population distributions from potentially suitable habitat, as well as how this might change with variation in environmental factors, which in turn allows dynamic evaluation of fisheries interactions in space and time (Sims 2010; Queiroz et al. 2016).
Consequently future efforts to explain movement behaviour should endeavour to incorporate factors that might explain the patterns being observed, for instance by also using data-loggers that record acceleration and gastric pH to detect feeding events (Papastamatiou, Meyer &
Holland 2007; Wilson, Shepard & Liebsch 2008; Sims 2010; Papastamatiou & Lowe 2012).
There has also been a tendency in the literature to focus on charismatic species, despite the general ecological importance of sharks (Simpfendorfer et al. 2011). Nonetheless it is evident
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that movement data is useful for determining whether management efforts are best focused on MPA development or modification of fishing practices, or indeed a combination of the two.